# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the BSD 3-Clause License  (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import warnings
from numbers import Number

import mmcv
import numpy as np
import torch
from mmcv import DictAction
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                         wrap_fp16_model)

from mmcls.apis import multi_gpu_test, single_gpu_test
from mmcls.datasets import build_dataloader, build_dataset
from mmcls.models import build_classifier
from mmcls.utils import (auto_select_device, get_root_logger,
                         setup_multi_processes, wrap_distributed_model,
                         wrap_non_distributed_model)


def parse_args():
    parser = argparse.ArgumentParser(description='mmcls test model')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', help='checkpoint file')
    parser.add_argument('--out', help='output result file')
    out_options = ['class_scores', 'pred_score', 'pred_label', 'pred_class']
    parser.add_argument(
        '--out-items',
        nargs='+',
        default=['all'],
        choices=out_options + ['none', 'all'],
        help='Besides metrics, what items will be included in the output '
        f'result file. You can choose some of ({", ".join(out_options)}), '
        'or use "all" to include all above, or use "none" to disable all of '
        'above. Defaults to output all.',
        metavar='')
    parser.add_argument(
        '--metrics',
        type=str,
        nargs='+',
        help='evaluation metrics, which depends on the dataset, e.g., '
        '"accuracy", "precision", "recall", "f1_score", "support" for single '
        'label dataset, and "mAP", "CP", "CR", "CF1", "OP", "OR", "OF1" for '
        'multi-label dataset')
    parser.add_argument('--show', action='store_true', help='show results')
    parser.add_argument(
        '--show-dir', help='directory where painted images will be saved')
    parser.add_argument(
        '--gpu-collect',
        action='store_true',
        help='whether to use gpu to collect results')
    parser.add_argument('--tmpdir', help='tmp dir for writing some results')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    parser.add_argument(
        '--metric-options',
        nargs='+',
        action=DictAction,
        default={},
        help='custom options for evaluation, the key-value pair in xxx=yyy '
        'format will be parsed as a dict metric_options for dataset.evaluate()'
        ' function.')
    parser.add_argument(
        '--show-options',
        nargs='+',
        action=DictAction,
        help='custom options for show_result. key-value pair in xxx=yyy.'
        'Check available options in `model.show_result`.')
    parser.add_argument(
        '--gpu-ids',
        type=int,
        nargs='+',
        help='(Deprecated, please use --gpu-id) ids of gpus to use '
        '(only applicable to non-distributed testing)')
    parser.add_argument(
        '--gpu-id',
        type=int,
        default=0,
        help='id of gpu to use '
        '(only applicable to non-distributed testing)')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')
    parser.add_argument('--local_rank', type=int, default=0)
    parser.add_argument('--device', help='device used for testing')
    args = parser.parse_args()
    if 'LOCAL_RANK' not in os.environ:
        os.environ['LOCAL_RANK'] = str(args.local_rank)

    assert args.metrics or args.out, \
        'Please specify at least one of output path and evaluation metrics.'

    return args


def main():
    args = parse_args()

    cfg = mmcv.Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)

    # set multi-process settings
    setup_multi_processes(cfg)

    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    cfg.model.pretrained = None

    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids[0:1]
        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
                      'Because we only support single GPU mode in '
                      'non-distributed testing. Use the first GPU '
                      'in `gpu_ids` now.')
    else:
        cfg.gpu_ids = [args.gpu_id]
    cfg.device = args.device or auto_select_device()

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    dataset = build_dataset(cfg.data.test, default_args=dict(test_mode=True))

    # build the dataloader
    # The default loader config
    loader_cfg = dict(
        # cfg.gpus will be ignored if distributed
        num_gpus=1 if cfg.device == 'ipu' else len(cfg.gpu_ids),
        dist=distributed,
        round_up=True,
    )
    # The overall dataloader settings
    loader_cfg.update({
        k: v
        for k, v in cfg.data.items() if k not in [
            'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
            'test_dataloader'
        ]
    })
    test_loader_cfg = {
        **loader_cfg,
        'shuffle': False,  # Not shuffle by default
        'sampler_cfg': None,  # Not use sampler by default
        **cfg.data.get('test_dataloader', {}),
    }
    # the extra round_up data will be removed during gpu/cpu collect
    data_loader = build_dataloader(dataset, **test_loader_cfg)

    # build the model and load checkpoint
    model = build_classifier(cfg.model)
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')

    if 'CLASSES' in checkpoint.get('meta', {}):
        CLASSES = checkpoint['meta']['CLASSES']
    else:
        from mmcls.datasets import ImageNet
        warnings.simplefilter('once')
        warnings.warn('Class names are not saved in the checkpoint\'s '
                      'meta data, use imagenet by default.')
        CLASSES = ImageNet.CLASSES

    if not distributed:
        model = wrap_non_distributed_model(
            model, device=cfg.device, device_ids=cfg.gpu_ids)
        if cfg.device == 'ipu':
            from mmcv.device.ipu import cfg2options, ipu_model_wrapper
            opts = cfg2options(cfg.runner.get('options_cfg', {}))
            if fp16_cfg is not None:
                model.half()
            model = ipu_model_wrapper(model, opts, fp16_cfg=fp16_cfg)
            data_loader.init(opts['inference'])
        model.CLASSES = CLASSES
        show_kwargs = args.show_options or {}
        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
                                  **show_kwargs)
    else:
        model = wrap_distributed_model(
            model,
            device=cfg.device,
            device_ids=[int(os.environ['LOCAL_RANK'])],
            broadcast_buffers=False)
        outputs = multi_gpu_test(model, data_loader, args.tmpdir,
                                 args.gpu_collect)

    rank, _ = get_dist_info()
    if rank == 0:
        results = {}
        logger = get_root_logger()
        if args.metrics:
            eval_results = dataset.evaluate(
                results=outputs,
                metric=args.metrics,
                metric_options=args.metric_options,
                logger=logger)
            results.update(eval_results)
            for k, v in eval_results.items():
                if isinstance(v, np.ndarray):
                    v = [round(out, 2) for out in v.tolist()]
                elif isinstance(v, Number):
                    v = round(v, 2)
                else:
                    raise ValueError(f'Unsupport metric type: {type(v)}')
                print(f'\n{k} : {v}')
        if args.out:
            if 'none' not in args.out_items:
                scores = np.vstack(outputs)
                pred_score = np.max(scores, axis=1)
                pred_label = np.argmax(scores, axis=1)
                pred_class = [CLASSES[lb] for lb in pred_label]
                res_items = {
                    'class_scores': scores,
                    'pred_score': pred_score,
                    'pred_label': pred_label,
                    'pred_class': pred_class
                }
                if 'all' in args.out_items:
                    results.update(res_items)
                else:
                    for key in args.out_items:
                        results[key] = res_items[key]
            print(f'\ndumping results to {args.out}')
            mmcv.dump(results, args.out)


if __name__ == '__main__':
    main()
